Ultrasound diagnosis apparatus, image processing apparatus, and image processing method

ABSTRACT

An ultrasound diagnosis apparatus according to an embodiment includes a decomposing unit, a diffusion filter unit, an adjusting unit, and a reconstructing unit. The decomposing unit decomposes ultrasound image data into low-frequency and high-frequency decomposed image data at each of a predetermined number of levels, by a multi-resolution analysis. The diffusion filter unit applies a diffusion filter to the low-frequency and high-frequency decomposed image data at the lowest level and applies, at each of the levels other than the lowest, a diffusion filter to data output from the level immediately underneath and to the high-frequency decomposed image data, and also generates edge information for each of the levels. The adjusting unit adjusts a signal level of the high-frequency decomposed image data for each of the levels, based on the edge information. The reconstructing unit obtains corrected data of the ultrasound image data by performing a multi-resolution synthesis.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of PCT international application Ser.No. PCT/JP2012/066886 filed on Jul. 2, 2012 which designates the UnitedStates, incorporated herein by reference, and which claims the benefitof priority from Japanese Patent Application No. 2011-146254, filed onJun. 30, 2011, the entire contents of which are incorporated herein byreference.

FIELD

Embodiments described herein relate generally to an ultrasound diagnosisapparatus, an image processing apparatus, and an image processingmethod.

BACKGROUND

As a process to eliminate speckles occurring in an ultrasound image, afiltering process in which a multi-resolution analysis is combined witha non-linear anisotropic diffusion filter is conventionally known.

During a diffusion filtering process that uses a non-linear anisotropicdiffusion filter, by applying mutually-different processes to an edgeportion and to portions other than the edge portion, it is possible toobtain an image in which the edge is enhanced and from which specklesare eliminated. Further, when a multi-resolution analysis is performed,by sequentially performing processes from a broad-perspective processtargeting a low-resolution image to a localized process targeting ahigh-resolution image, it is possible to perform the diffusion filteringprocess at a higher speed and more efficiently.

In other words, during the filtering process described above, thediffusion filtering process is applied either to the low-frequency imageresulting from the multi-resolution decomposition or to the high-ordermulti-resolution decomposed image, which is the data output from thelevel immediately underneath. In this situation, the non-linearanisotropic diffusion filter has a function to enhance the edge.However, when the diffusion filtering process is performed on an imagehaving a low spatial frequency, the structure in a broad perspective isto be enhanced. It is therefore difficult to apply a strong edgeenhancement.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a drawing for explaining an exemplary configuration of anultrasound diagnosis apparatus according to a present embodiment;

FIG. 2 is a drawing for explaining an exemplary functional configurationof an image processing unit in a situation where the number of levels isset to “3”;

FIG. 3 is a drawing for explaining a conventional method;

FIG. 4A and FIG. 4B are drawings for explaining the problem of theconventional method;

FIG. 5 is a drawing for explaining a speckle eliminating processperformed by the image processing unit according to the presentembodiment;

FIG. 6A and FIG. 6B are drawings for explaining the advantageous effectsof the present embodiment;

FIG. 7 is a flowchart for explaining a process performed by adecomposing unit according to the present embodiment;

FIG. 8 is a flowchart for explaining a process performed at Level 3 bythe image processing unit according to the present embodiment;

FIG. 9 is a flowchart for explaining a process performed at Level 2 bythe image processing unit according to the present embodiment; and

FIG. 10 is a flowchart for explaining a process performed at Level 1 bythe image processing unit according to the present embodiment.

DETAILED DESCRIPTION

An ultrasound diagnosis apparatus according to an embodiment includes adecomposing unit, a diffusion filter unit, an adjusting unit, and areconstructing unit. The decomposing unit is configured to decomposeultrasound image data into low-frequency decomposed image data andhigh-frequency decomposed image data at each of a predetermined numberof hierarchical levels, by performing a hierarchical multi-resolutionanalysis. The diffusion filter unit is configured to apply, at a lowesthierarchical level of the predetermined number of hierarchical levels, anon-linear anisotropic diffusion filter to the low-frequency decomposedimage data and the high-frequency decomposed image data at the lowesthierarchical level, configured to apply, at each of the hierarchicallevels higher than the lowest hierarchical level, a non-linearanisotropic diffusion filter to data output from a hierarchical levelimmediately underneath that has been reconstructed by performing amulti-resolution analysis and to the high-frequency decomposed imagedata at that hierarchical level, and configured to generate, for each ofthe hierarchical levels, edge information of a signal either from thelow-frequency decomposed image data at the lowest hierarchical level orfrom the data output from the hierarchical level immediately underneath.The adjusting unit is configured to adjust a signal level of thehigh-frequency decomposed image data for each of the hierarchicallevels, based on the edge information obtained at each of thehierarchical levels. The reconstructing unit is configured to obtaincorrected data of the ultrasound image data by hierarchically performinga multi-resolution synthesis on data output from the diffusion filterunit and data output from the adjusting unit that are obtained at eachof the hierarchical levels.

In the following sections, exemplary embodiments of an ultrasounddiagnosis apparatus will be explained in detail, with reference to theaccompanying drawings.

First, a configuration of an ultrasound diagnosis apparatus according toan exemplary embodiment will be explained. FIG. 1 is a drawing forexplaining an exemplary configuration of the ultrasound diagnosisapparatus according to the present embodiment. As shown in FIG. 1, theultrasound diagnosis apparatus according to the present embodimentincludes an ultrasound probe 1, a monitor 2, an input device 3, and anapparatus main body 10.

The ultrasound probe 1 includes a plurality of piezoelectric transducerelements, which generate an ultrasound wave based on a drive signalsupplied from a transmitting unit 11 included in the apparatus main body10 (explained later). Further, the ultrasound probe 1 receives areflected wave from a subject P and converts the received reflected waveinto an electric signal. Further, the ultrasound probe 1 includesmatching layers included in the piezoelectric transducer elements, aswell as a backing material that prevents ultrasound waves frompropagating rearward from the piezoelectric transducer elements. Theultrasound probe 1 is detachably connected to the apparatus main body10.

When an ultrasound wave is transmitted from the ultrasound probe 1 tothe subject P, the transmitted ultrasound wave is repeatedly reflectedon a surface of discontinuity of acoustic impedances at a tissue in thebody of the subject P and is received as a reflected-wave signal by theplurality of piezoelectric transducer elements included in theultrasound probe 1. The amplitude of the received reflected-wave signalis dependent on the difference between the acoustic impedances on thesurface of discontinuity on which the ultrasound wave is reflected. Whenthe transmitted ultrasound pulse is reflected on the surface of aflowing bloodstream or a cardiac wall, the reflected-wave signal is, dueto the Doppler effect, subject to a frequency shift, depending on avelocity component of the moving object with respect to the ultrasoundwave transmission direction.

It should be noted that the present embodiment is applicable to asituation where the ultrasound probe 1 is an ultrasound probe configuredto scan the subject P two-dimensionally and to a situation where theultrasound probe 1 is an ultrasound probe configured to scan the subjectP three-dimensionally, while using the ultrasound waves. An example ofthe ultrasound probe 1 configured to scan the subject Pthree-dimensionally is a mechanical scan probe that scans the subject Pthree-dimensionally by causing a plurality of ultrasound transducerelements which scans the subject P two-dimensionally to swing at apredetermined angle (a swinging angle). Another example of theultrasound probe 1 configured to scan the subject P three-dimensionallyis a two-dimensional ultrasound probe (a 2D probe) that performs anultrasound scan on the subject P three-dimensionally by using aplurality of ultrasound transducer elements that are arranged in amatrix formation. The 2D probe is also able to scan the subject Ptwo-dimensionally by transmitting the ultrasound waves in a convergedmanner.

The input device 3 includes a mouse, a keyboard, a button, a panelswitch, a touch command screen, a foot switch, a trackball, a joystick,and the like. The input device 3 receives various types of settingrequests from an operator of the ultrasound diagnosis apparatus andtransfers the received various types of setting requests to theapparatus main body 10.

The monitor 2 displays a Graphical User Interface (GUI) used by theoperator of the ultrasound diagnosis apparatus to input the varioustypes of setting requests through the input device 3 and displays anultrasound image and the like generated by the apparatus main body 10.

The apparatus main body 10 is an apparatus that generates the ultrasoundimage based on the reflected wave received by the ultrasound probe 1. Asshown in FIG. 1, the apparatus main body 10 includes the transmittingunit 11, a receiving unit 12, a B-mode processing unit 13, a Dopplerprocessing unit 14, an image generating unit 15, an image processingunit 16, an image memory 17, a controlling unit 18, and an internalstorage unit 19.

The transmitting unit 11 includes a trigger generating circuit, atransmission delaying circuit, a pulser circuit, and the like andsupplies the drive signal to the ultrasound probe 1. The pulser circuitrepeatedly generates a rate pulse for forming a transmission ultrasoundwave at a predetermined rate frequency. The transmission delayingcircuit applies a delay time that is required to converge the ultrasoundwave generated by the ultrasound probe 1 into the form of a beam and todetermine transmission directionality and that corresponds to each ofthe piezoelectric transducer elements, to each of the rate pulsesgenerated by the pulser circuit. Further, the trigger generating circuitapplies a drive signal (a drive pulse) to the ultrasound probe 1 withtiming based on the rate pulses. In other words, the transmissiondelaying circuit arbitrarily adjusts the directions of the transmissionsfrom the surface of the piezoelectric transducer element, by varying thedelay time applied to the rate pulses.

The transmitting unit 11 has a function to be able to instantly changethe transmission frequency, the transmission drive voltage, and thelike, for the purpose of executing a predetermined scanning sequencebased on an instruction from the controlling unit 18 (explained later).In particular, the configuration to change the transmission drivevoltage is realized by using a linear-amplifier-type transmittingcircuit of which the value can be instantly switched or by using amechanism configured to electrically switch between a plurality of powersource units.

The receiving unit 12 includes an amplifier circuit, an Analog/Digital(A/D) converter, an adder, and the like and generates reflected-wavedata by performing various types of processes on the reflected-wavesignal received by the ultrasound probe 1. The amplifier circuitamplifies the reflected-wave signal for each of channels and performs again correcting process thereon. The A/D converter applies an A/Dconversion to the gain-corrected reflected-wave signal and applies adelay time required to determine reception directionality to digitaldata. The adder generates the reflected-wave data by performing anadding process on the reflected-wave signals processed by the A/Dconverter. As a result of the adding process performed by the adder,reflected components from the direction corresponding to the receptiondirectionality of the reflected-wave signal are emphasized.

In this manner, the transmitting unit 11 and the receiving unit 12control the transmission directionality and the reception directionalityin the transmission and the reception of the ultrasound wave.

In this situation, if the ultrasound probe 1 is configured to be able toperform a three-dimensional scan, the transmitting unit 11 and thereceiving unit 12 are each able to also cause a three-dimensionalultrasound beam to be transmitted from the ultrasound probe 1 to thesubject P, so that three-dimensional reflected-wave data is generatedfrom three-dimensional reflected-wave signals received by the ultrasoundprobe 1.

The B-mode processing unit 13 receives the reflected-wave data from thereceiving unit 12 and generates data (B-mode data) in which the strengthof each signal is expressed by a degree of brightness, by performing alogarithmic amplification, an envelope detection process, and the likeon the received reflected-wave data.

The Doppler processing unit 14 extracts bloodstreams, tissues, andcontrast echo components under the influence of the Doppler effect byperforming a frequency analysis so as to obtain velocity informationfrom the reflected-wave data received from the receiving unit 12, andfurther generates data (Doppler data) obtained by extracting movingobject information such as an average velocity, the dispersion, thepower, and the like for a plurality of points.

The B-mode processing unit 13 and the Doppler processing unit 14according to the present embodiment are able to process bothtwo-dimensional reflected-wave data and three-dimensional reflected-wavedata. In other words, the B-mode processing unit 13 is able to generatetwo-dimensional B-mode data from two-dimensional reflected-wave data andis also able to generate three-dimensional B-mode data fromthree-dimensional reflected-wave data. The Doppler processing unit 14 isable to generate two-dimensional Doppler data from two-dimensionalreflected-wave data and is also able to generate three-dimensionalDoppler data from three-dimensional reflected-wave data.

The image generating unit 15 generates ultrasound image data based onthe reflected wave received by the ultrasound probe 1. In other words,the image generating unit 15 generates the ultrasound image data to beoutput to the monitor 2, from the data generated by the B-modeprocessing unit 13 and the Doppler processing unit 14. Morespecifically, from the two-dimensional B-mode data generated by theB-mode processing unit 13, the image generating unit 15 generates B-modeimage data in which the strength of the reflected wave is expressed by adegree of brightness. Further, from the two-dimensional Doppler datagenerated by the Doppler processing unit 14, the image generating unit15 generates an average velocity image, a dispersion image, and a powerimage, expressing the moving object information, or Doppler image data,which is an image combining these images.

In this situation, generally, the image generating unit 15 converts (byperforming a scan convert process) a scanning line signal sequence froman ultrasound scan into a scanning line signal sequence in a videoformat used by, for example, television and generates display-purposeultrasound image data. More specifically, the image generating unit 15generates the display-purpose ultrasound image data by performing acoordinate transformation process compliant with the ultrasound scanningform by the ultrasound probe 1. Further, the image generating unit 15synthesizes text information of various parameters, scale graduations,body marks, and the like with the display-purpose ultrasound image data.

In other words, the B-mode data and the Doppler data are the ultrasoundimage data before the scan convert process is performed. The datagenerated by the image generating unit 15 is the display-purposeultrasound image data obtained after the scan convert process isperformed. The B-mode data and the Doppler data may be referred to asraw data.

Further, the image generating unit 15 is also able to generatethree-dimensional ultrasound image data. In other words, the imagegenerating unit 15 is also able to generate three-dimensional B-modeimage data by performing a coordinate transformation process on thethree-dimensional B-mode data generated by the B-mode processing unit13. The image generating unit 15 is also able to generatethree-dimensional color Doppler image data by performing a coordinatetransformation process on the three-dimensional Doppler data generatedby the Doppler processing unit 14.

Further, the image generating unit 15 is also able to perform varioustypes of rendering processes on three-dimensional ultrasound image data(volume data). More specifically, the image generating unit 15 is ableto generate display-purpose two-dimensional ultrasound image data byperforming a rendering process on three-dimensional ultrasound imagedata. An example of the rendering process performed by the imagegenerating unit 15 is a process to reconstruct a Multi PlanarReconstruction (MPR) image by implementing an MPR method. Anotherexample of the rendering process performed by the image generating unit15 is a Volume Rendering (VR) process to generate a two-dimensionalimage in which three-dimensional information is reflected.

The image processing unit 16 is a processing unit that performs varioustypes of image processing on the ultrasound image data. In the presentembodiment, the image processing unit 16 performs processes to eliminatespeckles and to enhance the edge on the ultrasound image data.

To serve as a processing unit that performs such processes, the imageprocessing unit 16 includes a decomposing unit 161, a diffusion filterunit 162, an adjusting unit 163, and a reconstructing unit 164, as shownin FIG. 1. The decomposing unit 161 is a processing unit that decomposesimage data into low-frequency decomposed image data and high-frequencydecomposed image data by performing a multi-resolution analysis. Thediffusion filter unit 162 is a processing unit that detects edgeinformation from image data and applies a non-linear anisotropicdiffusion filter based on the detected edge information. The adjustingunit 163 is a processing unit that adjusts a signal level of the imagedata. The reconstructing unit 164 is a processing unit that performs areconstructing process to synthesize together low-frequency decomposedimage data and high-frequency decomposed image data by performing amulti-resolution analysis.

In the present embodiment, an example will be explained in which thedecomposing unit 161 performs a wavelet transform as a decomposingprocess realized by the multi-resolution analysis, whereas thereconstructing unit 164 performs a wavelet inverse transform as asynthesizing process (a reconstructing process) realized by themulti-resolution analysis. It should be noted, however, that the presentembodiment is also applicable to the situation where the decomposingunit 161 and the reconstructing unit 164 perform a multi-resolutiondecomposition and a multi-resolution synthesis by implementing aLaplacian pyramid method.

Further, the ultrasound image data used as a processing target by theimage processing unit 16 may be the raw data generated by the B-modeprocessing unit 13 and the Doppler processing unit 14 or may be thedisplay-purpose ultrasound image data generated by the image generatingunit 15. Processes performed by the image processing unit 16 accordingto the present embodiment will be explained later.

The image memory 17 is a memory for storing therein the ultrasound imagedata generated by the image generating unit 15 and processing results ofthe image processing unit 16. Further, the image memory 17 is also ableto store therein the raw data generated by the B-mode processing unit 13and the Doppler processing unit 14.

The internal storage unit 19 stores therein various types of data suchas a control program to realize ultrasound transmissions and receptions,image processing, and display processing, as well as diagnosisinformation (e.g., patients' IDs, medical doctors' observations),diagnosis protocols, and various types of body marks. Further, theinternal storage unit 19 may be used, as necessary, for storing thereinany of the images stored in the image memory 17. Furthermore, the datastored in the internal storage unit 19 can be transferred to anyexternal peripheral device via an interface circuit (not shown).

The controlling unit 18 is a controlling processor (a Central ProcessingUnit (CPU)) that realizes functions of an information processingapparatus and is configured to control the entire processes performed bythe ultrasound diagnosis apparatus. More specifically, based on thevarious types of setting requests input by the operator via the inputdevice 3 and various types of control programs and various types of dataread from the internal storage unit 19, the controlling unit 18 controlsprocesses performed by the transmitting unit 11, the receiving unit 12,the B-mode processing unit 13, the Doppler processing unit 14, the imagegenerating unit 15, and the image processing unit 16. Further, thecontrolling unit 18 exercises control so that the monitor 2 displays theultrasound image data stored in the image memory 17 and various types ofimage data stored in the internal storage unit 19, or a GUI used forrealizing the processes performed by the image processing unit 16 andthe processing results of the image processing unit 16, and the like.

An overall configuration of the ultrasound diagnosis apparatus accordingto the present embodiment has thus been explained. The ultrasounddiagnosis apparatus according to the present embodiment configured asdescribed above captures an ultrasound image by performing an ultrasoundtransmission and reception. In this situation, when reflecting objectsof which the size is very much smaller than the wavelength of thetransmitted ultrasound wave are densely present, the reflected-wavesignals interfere with one another. The magnitude of the interference isrepresented by a magnitude of the amplitude of the reflected-wavesignal. As a result, dot-like artifacts (speckles) occur in anultrasound image depicting such amplitude information.

Such speckles hinder an accurate visual perception of the position of aborder between living tissues or the shape of living tissues. For thisreason, various types of processes to eliminate speckles haveconventionally been performed. For example, according to a speckleeliminating method that uses a multi-resolution analysis (MRA), amulti-resolution decomposition is performed on ultrasound image data, sothat threshold processing or a weighting process is performed onhigh-frequency components of the decomposed image at each of thedifferent levels. As a result, the speckles are eliminated; however,ultrasound images obtained by displaying ultrasound image data processedin such a manner may seem artificial to viewers.

As another example, according to a speckle eliminating method that usesa non-linear anisotropic diffusion filter, mutually-different processesare applied to an edge portion (a border potion between tissues) and toportions other than the edge portion. As a result, it is possible toobtain an image in which the edge is enhanced and from which thespeckles are eliminated. However, because the non-linear anisotropicdiffusion filter requires a calculation process to obtain a solution ofa partial differential equation, the computing process takes time. Inaddition, when the process using the non-linear anisotropic diffusionfilter is performed alone, although a certain degree of speckle reducingeffect is achieved, the level of speckle eliminating effect may not besufficiently high.

To cope with this situation, in recent years, a speckle eliminatingmethod has been developed by which a filtering process is performed bycombining a multi-resolution analysis with a non-linear anisotropicdiffusion filter. In the following sections, the speckle eliminatingmethod by which the filtering process is performed by combining amulti-resolution analysis with a non-linear anisotropic diffusion filterwill be referred to as “the conventional method”.

According to the conventional method, for example, ultrasound image datais multi-resolution decomposed into low-frequency decomposed image dataand high-frequency decomposed image data at a predetermined number ofhierarchical levels (predetermined multiple levels), by performing awavelet transform. Further, according to the conventional method,processes using non-linear anisotropic diffusion filters aresequentially performed from the image data in a lower level to the imagedata in an upper level.

Next, an example in which the image processing unit 16 illustrated inFIG. 1 implements the conventional method will be explained. Forexample, if the number of levels in the multi-resolution decompositionis “3”, the decomposing unit 161, the diffusion filter unit 162, theadjusting unit 163, and the reconstructing unit 164 included in theimage processing unit 16 illustrated in FIG. 1 have a functionalconfiguration illustrated in FIG. 2, under the control of thecontrolling unit 18. FIG. 2 is a drawing for explaining the exemplaryfunctional configuration of the image processing unit in the situationwhere the number of levels is set to “3”.

When the number of levels is set to “3”, the decomposing unit 161 isconfigured as three functional processing units that are namely a firstdecomposing unit 161 a, a second decomposing unit 161 b, and a thirddecomposing unit 161 c, as shown in FIG. 2, so as to perform processesat Level 1, Level 2, and Level 3, respectively. Further, the diffusionfilter unit 162 is configured as three functional processing units thatare namely a first diffusion filter unit 162 a, a second diffusionfilter unit 162 b, and a third diffusion filter unit 162 c, as shown inFIG. 2, so as to perform processes at Level 1, Level 2, and Level 3,respectively.

In addition, the adjusting unit 163 is configured as three functionalprocessing units that are namely a first adjusting unit 163 a, a secondadjusting unit 163 b, and a third adjusting unit 163 c, as shown in FIG.2, so as to perform processes at Level 1, Level 2, and Level 3,respectively. Furthermore, the reconstructing unit 164 is configured asthree functional processing units that are namely a first reconstructingunit 164 a, a second reconstructing unit 164 b, and a thirdreconstructing unit 164 c, as shown in FIG. 2, so as to performprocesses at Level 1, Level 2, and Level 3, respectively.

FIG. 3 is a drawing for explaining the conventional method. In oneexample illustrated in FIG. 3, original image data used by the imageprocessing unit 16 as a processing target is B-mode data generated bythe B-mode processing unit 13.

The first decomposing unit 161 a decomposes the B-mode data intolow-frequency decomposed image data and high-frequency decomposed imagedata by performing a multi-resolution analysis. More specifically, byperforming a wavelet transform (a discrete wavelet transform), the firstdecomposing unit 161 a decomposes the B-mode data into “LL”, which is apiece of low-frequency decomposed image data, and “HL, LH, and HH”,which are pieces of high-frequency decomposed image data. In thissituation, “LL” is a piece of image data in which the components in boththe horizontal direction and the vertical direction are low-frequencycomponents. “HL” is a piece of image data in which the component in thehorizontal direction is a high-frequency component, whereas thecomponent in the vertical direction is a low-frequency component. “LH”is a piece of image data in which the component in the horizontaldirection is a low-frequency component, whereas the component in thevertical direction is a high-frequency component. “HH” is a piece ofimage data in which the components in both the horizontal direction andthe vertical direction are high-frequency components.

As shown in FIG. 3, the first decomposing unit 161 a outputs “LL(first)”(hereinafter, “LL(1st)”), which is LL at Level 1, to the seconddecomposing unit 161 b and outputs “HL(1st), LH(1st), and HH(1st)”,which are HL, LH, and HH at Level 1, to the first adjusting unit 163 a.

The second decomposing unit 161 b decomposes LL(1st) into low-frequencydecomposed image data and high-frequency decomposed image data. In otherwords, as shown in FIG. 3, the second decomposing unit 161 b decomposesLL(1st) into “LL(second)” (hereinafter, “LL(2nd)”), which is a piece oflow-frequency decomposed image data at Level 2 and “HL(2nd), LH(2nd),and HH(2nd)”, which are pieces of high-frequency decomposed image dataat Level 2. Further, as shown in FIG. 3, the second decomposing unit 161b outputs “LL(2nd)” to the third decomposing unit 161 c and outputs“HL(2nd), LH(2nd), and HH(2nd)” to the second adjusting unit 163 b.

The third decomposing unit 161 c decomposes LL(2nd) into low-frequencydecomposed image data and high-frequency decomposed image data. In otherwords, as shown in FIG. 3, the third decomposing unit 161 c decomposesLL(2nd) into “LL(third)” (hereinafter, “LL(3rd)”), which is a piece oflow-frequency decomposed image data at Level 3 and “HL(3rd), LH(3rd),and HH(3rd)”, which are pieces of high-frequency decomposed image dataat Level 3. Further, as shown in FIG. 3, the third decomposing unit 161c outputs “LL(3rd)” to the third diffusion filter unit 162 c and outputs“HL(3rd), LH(3rd), and HH(3rd)” to the third adjusting unit 163 c.

As a result of the multi-resolution decomposition, the decomposed imagedata has half the dimensions lengthwise and widthwise, compared to thedimensions prior to the decomposition. In other words, the resolution ofthe image data at Level 1 is “(½)×(½)=¼” of the resolution of the B-modedata; the resolution of the image data at Level 2 is “(¼)×(¼)= 1/16” ofthe resolution of the B-mode data; and the resolution of the image dataat Level 3 is “(⅛)×(⅛)= 1/64” of the resolution of the B-mode data.

After the multi-resolution decomposition is performed, the processes areperformed in the order of Level 3, Level 2, and Level 1. As shown inFIG. 3, the third diffusion filter unit 162 c calculates a structuretensor by using “LL(3rd)” and detects information (“edge information”)related to the edge of “LL(3rd)” from the structure tensor. After that,as shown in FIG. 3, the third diffusion filter unit 162 c calculates adiffusion tensor from the structure tensor and the edge information andapplies a non-linear anisotropic diffusion filter (3rd) to “LL(3rd)” byusing the calculated diffusion tensor. Subsequently, as shown in FIG. 3,the third diffusion filter unit 162 c outputs “filtered LL(3rd)” to thethird reconstructing unit 164 c.

Further, as shown in FIG. 3, the third adjusting unit 163 c adjustssignal levels of “HL(3rd), LH(3rd), and HH(3rd)” by using the edgeinformation detected by the third diffusion filter unit 162 c. Afterthat, as shown in FIG. 3, the third adjusting unit 163 c outputs“adjusted HL(3rd), adjusted LH(3rd), and adjusted HH(3rd)” to the thirdreconstructing unit 164 c.

As shown in FIG. 3, the third reconstructing unit 164 c reconstructs“filtered LL(3rd)” and “adjusted HL(3rd), adjusted LH(3rd), and adjustedHH(3rd)” by performing a multi-resolution synthesis. More specifically,by performing a wavelet inverse transform, the third reconstructing unit164 c synthesizes together “filtered LL(3rd)” and “adjusted HL(3rd),adjusted LH(3rd), and adjusted HH(3rd)”. After that, as shown in FIG. 3,the third reconstructing unit 164 c outputs “Level 3 output data”, whichis the data resulting from the reconstruction, to the second diffusionfilter unit 162 b at Level 2. As a result of the process performed bythe third reconstructing unit 164 c, the resolution of the “Level 3output data” is “ 1/16” of the resolution of the B-mode data.

At Level 2, as shown in FIG. 3, the second diffusion filter unit 162 bcalculates a structure tensor by using the “Level 3 output data” anddetects edge information of the “Level 3 output data” from the structuretensor. After that, as shown in FIG. 3, the second diffusion filter unit162 b calculates a diffusion tensor from the structure tensor and theedge information and applies a non-linear anisotropic diffusion filter(2nd) to the “Level 3 output data” by using the calculated diffusiontensor. Subsequently, as shown in FIG. 3, the second diffusion filterunit 162 b outputs “filtered Level 3 output data” to the secondreconstructing unit 164 b.

Further, as shown in FIG. 3, the second adjusting unit 163 b adjustssignal levels of “HL(2nd), LH(2nd), and HH(2nd)” by using the edgeinformation detected by the second diffusion filter unit 162 b. Afterthat, as shown in FIG. 3, the second adjusting unit 163 b outputs“adjusted HL(2nd), adjusted LH(2nd), and adjusted HH(2nd)” to the secondreconstructing unit 164 b.

As shown in FIG. 3, by performing a wavelet inverse transform, thesecond reconstructing unit 164 b synthesizes together “filtered Level 3output data” and “adjusted HL(2nd), adjusted LH(2nd), and adjustedHH(2nd)”. After that, as shown in FIG. 3, the second reconstructing unit164 b outputs “Level 2 output data”, which is the data resulting fromthe reconstruction, to the first diffusion filter unit 162 a at Level 1.As a result of the process performed by the second reconstructing unit164 b, the resolution of the “Level 2 output data” is “¼” of theresolution of the B-mode data.

At Level 1, as shown in FIG. 3, the first diffusion filter unit 162 acalculates a structure tensor by using the “Level 2 output data” anddetects edge information of the “Level 2 output data” from the structuretensor. After that, as shown in FIG. 3, the first diffusion filter unit162 a calculates a diffusion tensor from the structure tensor and theedge information and applies a non-linear anisotropic diffusion filter(1st) to the “Level 2 output data” by using the calculated diffusiontensor. Subsequently, as shown in FIG. 3, the first diffusion filterunit 162 a outputs “filtered Level 2 output data” to the firstreconstructing unit 164 a.

Further, as shown in FIG. 3, the first adjusting unit 163 a adjustssignal levels of “HL(1st), LH(1st), and HH(1st)” by using the edgeinformation detected by the first diffusion filter unit 162 a. Afterthat, as shown in FIG. 3, the first adjusting unit 163 a outputs“adjusted HL(1st), adjusted LH(1st), and adjusted HH(1st)” to the firstreconstructing unit 164 a.

As shown in FIG. 3, by performing a wavelet inverse transform, the firstreconstructing unit 164 a synthesizes together “filtered Level 2 outputdata” and “adjusted HL(1st), adjusted LH(1st), and adjusted HH(1st)”.After that, as shown in FIG. 3, the first reconstructing unit 164 aoutputs “Level 1 output data”, which is the data resulting from thereconstruction. More specifically, the first reconstructing unit 164 aoutputs the “Level 1 output data” to the image generating unit 15 as“corrected B-mode data”. As a result of the process performed by thefirst reconstructing unit 164 a, the resolution of the “Level 1 outputdata” is equal to the resolution of the B-mode data. By performing ascan convert process on the corrected B-mode data, the image generatingunit 15 generates display-purpose ultrasound image data.

As explained above, according to the conventional method illustrated inFIG. 3, the diffusion filtering process is applied either to thelow-frequency decomposed image data resulting from the multi-resolutiondecomposition or to the higher-order multi-resolution decomposed image,which is the data output from the level immediately underneath. As aresult, according to the conventional method, it is possible to performthe diffusion filtering process at a high speed and efficiently.Further, due to a synergistic effect of the multi-resolution analysisand the non-linear anisotropic diffusion filtering process, it ispossible to eliminate the speckles precisely.

However, according to the conventional method described above, becausethe edge enhancing process using the non-linear anisotropic diffusionfilter is performed also on the image having a low spatial frequencysuch as LL(3rd), for example, the structure in a broad perspective isenhanced, which is not desirable. For instance, when a strong edgeenhancement is applied according to the conventional method describedabove, a diagonal structure is enhanced so as to seem like astair-stepped structure. FIGS. 4A and 4B are drawings for explaining theproblem of the conventional method.

As explained above, the resolution of the image data at Level 3 is “1/64” of the resolution of the B-mode data. For this reason, forexample, if the edge detection result at Level 3 (see the area with gridhatching in the drawing) is superimposed while being enlarged to thenumber of pixels of the B-mode data (Level 0) as shown in FIG. 4A, theedge portion of the diagonal structure becomes stair-stepped. As aresult, when a strong edge enhancement is applied at each of the levels,a diagonal structure rendered in the ultrasound image displayed on themonitor 2 will have a stair-stepped form and not the real form, as shownin FIG. 4B. Consequently, the result of the edge enhancement rendered inthe ultrasound image generated according to the conventional methodmakes the viewer feel that something is wrong with the ultrasound image,in some situations.

To cope with these situations, the decomposing unit 161, the diffusionfilter unit 162, the adjusting unit 163, and the reconstructing unit 164included in the image processing unit 16 according to the presentembodiment performs processes as described below, to generate anultrasound image in which the edge is enhanced without causing thefeeling that something is wrong with the ultrasound image and from whichspeckles are eliminated.

The decomposing unit 161 according to the present embodiment decomposesultrasound image data into low-frequency decomposed image data andhigh-frequency decomposed image data at each of a predetermined numberof hierarchical levels, by performing a hierarchical multi-resolutionanalysis.

Further, at the lowest hierarchical level of the predetermined number ofhierarchical levels, the diffusion filter unit 162 according to thepresent embodiment applies a non-linear anisotropic diffusion filter tothe low-frequency decomposed image data and the high-frequencydecomposed image data at the lowest hierarchical level. Further, at eachof the hierarchical levels higher than the lower hierarchical level, thediffusion filter unit 162 according to the present embodiment applies anon-linear anisotropic diffusion filter to the data output from thehierarchical level immediately underneath that has been reconstructed byperforming a multi-resolution analysis and to the high-frequencydecomposed image data at that hierarchical level. Further, in additionto applying the non-linear anisotropic diffusion filter, the diffusionfilter unit 162 according to the present embodiment generates (detects),for each of the hierarchical levels, edge information of the signaleither from the low-frequency decomposed image data at the lowerhierarchical level or from the data output from the hierarchical levelimmediately underneath.

Further, based on the edge information obtained at each of thehierarchical levels, the adjusting unit 163 according to the presentembodiment adjusts the signal level of the high-frequency decomposedimage data for each of the hierarchical levels. More specifically, theadjusting unit 163 according to the present embodiment adjusts thesignal level of the high-frequency decomposed image data to which thenon-linear anisotropic diffusion filter was applied, based on the edgeinformation detected by the diffusion filter unit 162 when performingthe non-linear anisotropic diffusion filtering process on the samehierarchical level as the hierarchical level of the high-frequencydecomposed image data. Even more specifically, at the lowesthierarchical level, the adjusting unit 163 according to the presentembodiment adjusts the signal level of the high-frequency decomposedimage data to which the non-linear anisotropic diffusion filter wasapplied, based on the edge information detected by the diffusion filterunit 162 from the low-frequency decomposed image data at the lowesthierarchical level. In contrast, at each of the hierarchical levelshigher than the lowest hierarchical level, the adjusting unit 163according to the present embodiment adjusts the signal level of thehigh-frequency decomposed image data to which the non-linear anisotropicdiffusion filter was applied, based on the edge information detected bythe diffusion filter unit 162 from the data output from the hierarchicallevel immediately underneath.

Further, the reconstructing unit 164 according to the present embodimentobtains corrected data of the ultrasound image data by hierarchicallyperforming a multi-resolution synthesis on the data output from thediffusion filter unit 162 and the data output from the adjusting unit163 that are obtained at each of the hierarchical levels. Morespecifically, the reconstructing unit 164 according to the presentembodiment reconstructs the data by performing the multi-resolutionanalysis, from the data that has been processed by the diffusion filterunit 162 and was not used in the process performed by the adjusting unit163 and the data that has been processed by the adjusting unit 163 atthe same hierarchical level as such data. After that, at each of thehierarchical levels lower than the highest hierarchical level of thepredetermined number of hierarchical levels, the reconstructing unit 164according to the present embodiment outputs the reconstructed data asthe output data to be processed by the diffusion filter unit 162 at thehierarchical level immediately above. In contrast, at the highesthierarchical level, the reconstructing unit 164 according to the presentembodiment outputs the reconstructed data as corrected data of theultrasound image data.

More specifically, at the lowest hierarchical level, the diffusionfilter unit 162 according to the present embodiment applies thenon-linear anisotropic diffusion filter to the high-frequency decomposedimage data at the lowest hierarchical level by using a diffusion filtercoefficient calculated based on a structure tensor and edge informationdetected from the low-frequency decomposed image data at the lowesthierarchical level. In contrast, at each of the hierarchical levelshigher than the lowest hierarchical level, the diffusion filter unit 162according to the present embodiment applies the non-linear anisotropicdiffusion filter to the high-frequency decomposed image data at thathierarchical level by using a diffusion filter coefficient calculatedbased on a structure tensor and edge information detected from theoutput data that was output by the reconstructing unit at thehierarchical level immediately underneath.

Next, processes performed by the decomposing unit 161, the diffusionfilter unit 162, the adjusting unit 163, and the reconstructing unit 164according to the present embodiment will be explained with reference toFIG. 5, by using an example in which the number of hierarchical levels(the number of levels) is set to “3”, and the ultrasound image dataserving as a processing target is B-mode data. FIG. 5 is a drawing forexplaining a speckle eliminating process performed by the imageprocessing unit according to the present embodiment.

To make the differences from the conventional method clear, the presentembodiment will be explained while using the same reference charactersas those of the processing units used in the explanation of theconventional method above. In other words, when the number of levels isset to “3”, the decomposing unit 161, the diffusion filter unit 162, theadjusting unit 163, and the reconstructing unit 164 are each configuredas the three functional processing units as shown in FIG. 2, so as toperform the processes at Level 1, Level 2, and Level 3, respectively.

First, as shown in FIG. 5, by performing a wavelet transform (a discretewavelet transform), the first decomposing unit 161 a decomposes theB-mode data into “LL(1st)”, which is a piece of low-frequency decomposedimage data, and “HL(1st), LH(1st), and HH(1st)”, which are pieces ofhigh-frequency decomposed image data. After that, as shown in FIG. 5,the first decomposing unit 161 a outputs “LL(1st)” to the seconddecomposing unit 161 b. Also, as shown in FIG. 5, the first decomposingunit 161 a outputs “HL(1st), LH(1st), and HH(1st)”, to the firstdiffusion filter unit 162 a.

As shown in FIG. 5, the second decomposing unit 161 b decomposes LL(1st)into “LL(2nd)” and “HL(2nd), LH(2nd), and HH(2nd)”. After that, as shownin FIG. 5, the second decomposing unit 161 b outputs “LL(2nd)” to thethird decomposing unit 161 c. Also, as shown in FIG. 5, the seconddecomposing unit 161 b outputs “HL(2nd), LH(2nd), and HH(2nd)”, to thesecond diffusion filter unit 162 b.

As shown in FIG. 5, the third decomposing unit 161 c decomposes LL(2nd)into “LL(3rd)” and “HL(3rd), LH(3rd), and HH(3rd)”. After that, as shownin FIG. 5, the third decomposing unit 161 c outputs “LL(3rd)” and“HL(3rd), LH(3rd), and HH(3rd)” to the third diffusion filter unit 162c.

After the multi-resolution decomposition is performed, the processes areperformed in the order of Level 3, Level 2, and Level 1. As shown inFIG. 5, the third diffusion filter unit 162 c calculates a structuretensor by using “LL(3rd)” and detects edge information of “LL(3rd)” fromthe structure tensor. After that, as shown in FIG. 5, the thirddiffusion filter unit 162 c calculates a diffusion tensor from thestructure tensor and the edge information and applies a non-linearanisotropic diffusion filter (3rd) to “LL(3rd)” by using the calculateddiffusion tensor. Subsequently, as shown in FIG. 5, the third diffusionfilter unit 162 c outputs “filtered LL(3rd)” to the third reconstructingunit 164 c.

Further, as shown in FIG. 5, the third diffusion filter unit 162 capplies a non-linear anisotropic diffusion filter (3rd) to “HL(3rd),LH(3rd), and HH(3rd)” by using a diffusion filter coefficient of thediffusion tensor calculated from “LL(3rd)” and outputs the result to thethird adjusting unit 163 c.

As shown in FIG. 5, the third adjusting unit 163 c adjusts signal levelsof “diffusion-filtered HL(3rd), diffusion-filtered LH(3rd), anddiffusion-filtered HH(3rd)” on which the diffusion filtering process wasperformed, by using the edge information detected by the third diffusionfilter unit 162 c. After that, as shown in FIG. 5, the third adjustingunit 163 c outputs “adjusted HL(3rd), adjusted LH(3rd), and adjustedHH(3rd)” to the third reconstructing unit 164 c.

As shown in FIG. 5, the third reconstructing unit 164 c synthesizestogether “filtered LL(3rd)” and “adjusted HL(3rd), adjusted LH(3rd), andadjusted HH(3rd)” by performing a wavelet inverse transform. After that,as shown in FIG. 5, the third reconstructing unit 164 c outputs “Level 3output data”, which is the data resulting from the reconstruction, tothe second diffusion filter unit 162 b at Level 2. The resolution of the“Level 3 output data” shown in FIG. 5 is “ 1/16” of the resolution ofthe B-mode data. It should be noted, however, that the “Level 3 outputdata” shown in FIG. 5 is, unlike in the conventional method, dataobtained by synthesizing together the data of which the signal levelswere adjusted after applying the non-linear anisotropic diffusion filterto the high-frequency decomposed image data at Level 3 and “filteredLL(3rd)”. The resolution of the “Level 3 output data” shown in FIG. 5 is“ 1/16” of the resolution of the B-mode data.

At Level 2, as shown in FIG. 5, the second diffusion filter unit 162 bcalculates a structure tensor by using the “Level 3 output data” anddetects edge information of the “Level 3 output data” from the structuretensor. After that, as shown in FIG. 5, the second diffusion filter unit162 b calculates a diffusion tensor from the structure tensor and theedge information and applies a non-linear anisotropic diffusion filter(2nd) to the “Level 3 output data” by using the calculated diffusiontensor. Subsequently, as shown in FIG. 5, the second diffusion filterunit 162 b outputs the “filtered Level 3 output data” to the secondreconstructing unit 164 b.

Further, as shown in FIG. 5, the second diffusion filter unit 162 bapplies a non-linear anisotropic diffusion filter (2nd) to “HL(2nd),LH(2nd), and HH(2nd)” by using a diffusion filter coefficient of thediffusion tensor calculated from the “Level 3 output data” and outputsthe result to the second adjusting unit 163 b.

As shown in FIG. 5, the second adjusting unit 163 b adjusts signallevels of “diffusion-filtered HL(2nd), diffusion-filtered LH(2nd), anddiffusion-filtered HH(2nd)” on which the diffusion filtering process wasperformed, by using the edge information detected by the seconddiffusion filter unit 162 b. After that, as shown in FIG. 5, the secondadjusting unit 163 b outputs “adjusted HL(2nd), adjusted LH(2nd), andadjusted HH(2nd)” to the second reconstructing unit 164 b.

As shown in FIG. 5, the second reconstructing unit 164 b synthesizestogether the “filtered Level 3 output data” and “adjusted HL(2nd),adjusted LH(2nd), and adjusted HH(2nd)” by performing a wavelet inversetransform. After that, as shown in FIG. 5, the second reconstructingunit 164 b outputs “Level 2 output data”, which is the data resultingfrom the reconstruction, to the first diffusion filter unit 162 a atLevel 1. The resolution of the “Level 2 output data” shown in FIG. 5 is“¼” of the resolution of the B-mode data. It should be noted, however,that the “Level 2 output data” shown in FIG. 5 is, unlike in theconventional method, data obtained by synthesizing together the data ofwhich the signal levels were adjusted after applying the non-linearanisotropic diffusion filter to the high-frequency decomposed image dataat Level 2 and the “filtered Level 3 output data”. The resolution of the“Level 2 output data” shown in FIG. 5 is “¼” of the resolution of theB-mode data.

At Level 1, as shown in FIG. 5, the first diffusion filter unit 162 acalculates a structure tensor by using the “Level 2 output data” anddetects edge information of the “Level 2 output data” from the structuretensor. After that, as shown in FIG. 5, the first diffusion filter unit162 a calculates a diffusion tensor from the structure tensor and theedge information and applies a non-linear anisotropic diffusion filter(1st) to the “Level 2 output data” by using the calculated diffusiontensor. Subsequently, as shown in FIG. 5, the first diffusion filterunit 162 a outputs the “filtered Level 2 output data” to the firstreconstructing unit 164 a.

Further, as shown in FIG. 5, the first diffusion filter unit 162 aapplies a non-linear anisotropic diffusion filter (1st) to “HL(1st),LH(1st), and HH(1st)” by using a diffusion filter coefficient of thediffusion tensor calculated from the “Level 2 output data” and outputsthe result to the first adjusting unit 163 a.

As shown in FIG. 5, the first adjusting unit 163 a adjusts signal levelsof “diffusion-filtered HL(1st), diffusion-filtered LH(1st), anddiffusion-filtered HH(1st)” on which the diffusion filtering process wasperformed, by using the edge information detected by the first diffusionfilter unit 162 a. After that, as shown in FIG. 5, the first adjustingunit 163 a outputs “adjusted HL(1st), adjusted LH(1st), and adjustedHH(1st)” to the first reconstructing unit 164 a.

As shown in FIG. 5, the first reconstructing unit 164 a synthesizestogether the “filtered Level 2 output data” and “adjusted HL(1st),adjusted LH(1st), and adjusted HH(1st)” by performing a wavelet inversetransform. After that, as shown in FIG. 5, the first reconstructing unit164 a outputs “Level 1 output data”, which is the data resulting fromthe reconstruction, to the image generating unit 15 as “corrected B-modedata”. The image generating unit 15 generates display-purpose ultrasoundimage data by performing a scan convert process on the corrected B-modedata. The resolution of the “Level 1 output data” shown in FIG. 5 isequal to the resolution of the B-mode data. It should be noted, however,that the “Level 1 output data” shown in FIG. 5 is, unlike in theconventional method, data obtained by synthesizing together the data ofwhich the signal levels were adjusted after applying the non-linearanisotropic diffusion filter to the high-frequency decomposed image dataat Level 1 and the “filtered Level 2 output data”. The resolution of the“Level 1 output data” shown in FIG. 5 is equal to the resolution of theB-mode data.

Next, the processes performed by the diffusion filter unit 162 and theadjusting unit 163 according to the present embodiment described abovewill be further explained by using mathematical expressions and thelike.

The diffusion filter unit 162 calculates the structure tensor bydifferentiating a pixel level (a brightness value) of the input imagedata in the horizontal direction (the widthwise direction or the xdirection) and the vertical direction (the lengthwise direction or the ydirection). The structure tensor is a tensor calculated for the purposeof detecting the magnitude and the direction of the edge. An eigenvalueof the structure tensor is associated with the magnitude of the edge,whereas an eigenvector of the structure tensor expresses the directionof the edge. A structure tensor “S” can be defined as shown inExpression (1) below:

$\begin{matrix}{S = {{{Gp}*\begin{pmatrix}I_{x}^{2} & {I_{x}I_{y}} \\{I_{x}I_{y}} & I_{x}^{2}\end{pmatrix}} = {\begin{pmatrix}{{Gp}*I_{x}^{2}} & {{Gp}*\left( {I_{x}I_{y}} \right)} \\{{Gp}*\left( {I_{x}I_{y}} \right)} & {{Gp}*I_{x}^{2}}\end{pmatrix} = \begin{pmatrix}s_{11} & s_{12} \\s_{12} & s_{22}\end{pmatrix}}}} & (1)\end{matrix}$

In this situation, “I_(x)” in Expression (1) denotes an x-directionspatial derivative of a pixel level “I” of the input image data, whereas“I_(y)” in Expression (1) denotes a y-direction spatial derivative of“I”. Further, “Gρ” denotes a two-dimensional Gaussian function, whereasthe operator “*” denotes a convolution. For example, the third diffusionfilter unit 162 c calculates the structure tensor “s₁₁, s₁₂, s₂₂” shownin Expression (1), by differentiating “LL(3rd)” in the horizontaldirection (the widthwise direction or the x direction) and the verticaldirection (the lengthwise direction or the y direction).

To calculate the structure tensor, it is not necessary to preciselyfollow the method shown above. Alternatively, it is also acceptable toapply a sobel filter at the first stage of the process, instead ofcalculating “I_(x)” and “I_(y)”.

After that, the diffusion filter unit 162 detects the edge information(the position, the magnitude, and the direction of the edge) from eachof the elements of the calculated structure tensor. More specifically,the diffusion filter unit 162 detects the edge information from each ofthe elements of the structure tensor and further calculates thediffusion filter coefficient used in the calculation of the diffusiontensor. After that, the diffusion filter unit 162 calculates thediffusion tensor. A diffusion tensor (D) can be defined as shown inExpression (2) below:

$\begin{matrix}{D = {\begin{pmatrix}d_{11} & d_{12} \\d_{12} & d_{22}\end{pmatrix} = {{R\begin{pmatrix}\lambda_{1} & 0 \\0 & \lambda_{2}\end{pmatrix}}R^{T}}}} & (2)\end{matrix}$

In this situation, “R” in Expression (2) denotes a rotation matrix,whereas “R^(T)” denotes a transposed matrix of “R”. Further, “λ₁, λ₂” inExpression (2) are diffusion filter coefficients calculated from theedge information. For example, the third diffusion filter unit 162 ccalculates a diffusion tensor “d₁₁, d₁₂, d₂₂” of “LL(3rd)” by usingExpression (2).

Further, the diffusion filter unit 162 applies a non-linear anisotropicdiffusion filter based on the diffusion tensor to “I”. The non-linearanisotropic diffusion filter can be expressed as shown in Expression (3)below, which is a partial differential equation.

$\begin{matrix}{\frac{\partial I}{\partial t} = {{div}\left\lbrack {D{\nabla I}} \right\rbrack}} & (3)\end{matrix}$

In this situation, “∇I (nabla I)” in Expression (3) is a gradient vectorof “I”, whereas “t” in Expression (3) is a time that is related to theprocess. Further, “div” in Expression (3) is a divergence.

In other words, the calculating process “D∇I” performed by the diffusionfilter unit 162 in Expression (3) is a calculating process to multiply aspecific direction with respect to the gradient vector of each of thepixels and the direction perpendicular to the specific direction by “λ₁”and “λ₂”, respectively. In this situation, the “specific direction” isthe direction of the edge of the image data, whereas the diffusionfilter coefficients are calculated according to the magnitude of theedge.

Further, the diffusion filter unit 162 performs the non-linearanisotropic diffusion filtering process by performing the numeric valueanalytical calculation using the partial differential equation shown inExpression (3) either once or multiple times repeatedly. For example, atthe time “t”, based on the pixel levels of a pixel at a point and aplurality of points (e.g., 9 points) in the surrounding of the pixel andthe values of the elements of the diffusion tensor, the diffusion filterunit 162 calculates new pixel levels of the points at a time “t+Δt”.After that, by using “t+Δt” as a new “t”, the diffusion unit 162 repeatsthe same calculation once or multiple times. For example, the thirddiffusion filter unit 162 c performs this non-linear anisotropicdiffusion filtering process on “LL(3rd)” and “HL(3rd), LH(3rd), andHH(3rd)”.

For the purpose of being able to change the method for calculating “λ₁,λ₂” depending on the characteristic of each of the ultrasound images indifferent diagnostic fields, it is desirable to prepare generalexpressions, so that it is possible to make adjustments with one or moreparameters.

Further, the adjusting unit 163 adjusts the signal levels of thediffusion-filtered high-frequency decomposed image data, i.e.,“diffusion-filtered HL, diffusion-filtered LH, and diffusion-filteredHH”, based on the “eigenvalue of the structure tensor serving as theedge information”. As described above, the eigenvalue of the structuretensor indicates the magnitude of the edge. Thus, the adjusting unit 163calculates, for each of the pixels, the product of the magnitude of theedge normalized based on the eigenvalue of the structure tensor and eachof the pieces of high-frequency decomposed image data. Further, bymultiplying the calculation result by a control coefficient set for eachof the pieces of high-frequency decomposed image data, the adjustingunit 163 performs a high-frequency level adjusting process.Alternatively, the adjusting unit 163 may perform a high-frequency leveladjusting process by setting a threshold value for the magnitude of theedge and, while considering components exceeding the threshold value asthe edge, multiplying the data in the area other than the edge by thecontrol coefficient corresponding to each of the pieces ofhigh-frequency decomposed image data.

As explained above, according to the present embodiment, the non-linearanisotropic diffusion filter is applied even to the high-frequencydecomposed image data. More specifically, according to the presentembodiment, the non-linear anisotropic diffusion filter is applied tothe high-frequency decomposed image data by using the structure tensorand the edge information of either the low-frequency decomposed imagedata or the reconstructed data from the level immediately underneath.FIGS. 6A and 6B are drawings for explaining advantageous effects of thepresent embodiment.

As a result of applying the non-linear anisotropic diffusion filter tothe high-frequency decomposed image data, it is possible to adjust thestructure extending in a diagonal direction, which seems unclear in thehigh-frequency decomposed image, so as to seem continuous in a diagonaldirection as shown in FIG. 6A. More specifically, by adjusting thediffusion filter coefficients, it is possible to apply an appropriateedge enhancement even to the high-frequency decomposed image data. Inthe present embodiment, the reconstructing process is performed afterperforming the signal level adjusting process on the high-frequencydecomposed image data to which the diffusion filtering process wasapplied in this manner. As a result, in the ultrasound image that isgenerated and displayed after the scan convert process is performed onthe corrected B-mode data output by the first reconstructing unit 164 aaccording to the first embodiment, the diagonal structure has asubstantially smooth shape, as shown in FIG. 6B.

Next, processes performed by the ultrasound diagnosis apparatusaccording to the present embodiment will be explained, with reference toFIGS. 7 to 10. FIG. 7 is a flowchart for explaining a process performedby the decomposing unit according to the present embodiment. FIG. 8 is aflowchart for explaining a process performed at Level 3 by the imageprocessing unit according to the present embodiment. FIG. 9 is aflowchart for explaining a process performed at Level 2 by the imageprocessing unit according to the present embodiment. FIG. 10 is aflowchart for explaining a process performed at Level 1 by the imageprocessing unit according to the present embodiment.

As shown in FIG. 7, the first decomposing unit 161 a included in theultrasound diagnosis apparatus according to the present embodimentjudges whether B-mode data has been stored into the image memory 17(step S101). In this situation, if no B-mode data has been stored (stepS101: No), the first decomposing unit 161 a goes into a standby state.

On the contrary, if B-mode data has been stored (step S101: Yes), thefirst decomposing unit 161 a decomposes the B-mode data intolow-frequency decomposed image data and high-frequency decomposed imagedata at Level 1, by performing a wavelet transform (step S102).

After that, the second decomposing unit 161 b decomposes thelow-frequency decomposed image data at Level 1 into low-frequencydecomposed image data and high-frequency decomposed image data at Level2, by performing a wavelet transform (step S103).

Subsequently, the third decomposing unit 161 c decomposes thelow-frequency decomposed image data at Level 2 into low-frequencydecomposed image data and high-frequency decomposed image data at Level3, by performing a wavelet transform (step S104), and the decomposingprocess is thus ended.

After that, the process at Level 3 is performed as shown in FIG. 8. Morespecifically, as shown in FIG. 8, the third diffusion filter unit 162 cjudges whether the low-frequency decomposed image data and thehigh-frequency decomposed image data at Level 3 have been obtained (stepS201). In this situation, if the judgment result is in the negative(step S201: No), the third diffusion filter unit 162 c goes into astandby state.

On the contrary, if the low-frequency decomposed image data and thehigh-frequency decomposed image data at Level 3 have been obtained (stepS201: Yes), the third diffusion filter unit 162 c calculates a structuretensor from the Level 3 low-frequency decomposed image data (step S202),and further detects edge information from the structure tensor (stepS203). Subsequently, the third diffusion filter unit 162 c calculates adiffusion tensor from the structure tensor and the edge information(step S204).

After that, the third diffusion filter unit 162 c performs a non-linearanisotropic diffusion filtering process on the Level 3 low-frequencydecomposed image data and the Level 3 high-frequency decomposed imagedata by using the diffusion tensor (step S205).

Further, the third adjusting unit 163 c adjusts the signal level of theLevel 3 high-frequency decomposed image data on which the diffusionfiltering process was performed (step S206). The third reconstructingunit 164 c reconstructs (synthesizes the data to obtain) Level 3 outputdata by performing a wavelet inverse transform (step S207). After that,the third reconstructing unit 164 c outputs the Level 3 output data tothe second diffusion filter unit 162 b (step S208), and the process atLevel 3 is thus ended.

After that, the process at Level 2 is performed as shown in FIG. 9. Morespecifically, as shown in FIG. 9, the second diffusion filter unit 162 bjudges whether the Level 2 high-frequency decomposed image data and theLevel 3 output data have been obtained (step S301). In this situation,if the judgment result is in the negative (step S301: No), the seconddiffusion filter unit 162 b goes into a standby state.

On the contrary, if the Level 2 high-frequency decomposed image data andthe Level 3 output data have been obtained (step S301: Yes), the seconddiffusion filter unit 162 b calculates a structure tensor from the Level3 output data (step S302), and further detects edge information from thestructure tensor (step S303). Subsequently, the second diffusion filterunit 162 b calculates a diffusion tensor from the structure tensor andthe edge information (step S304).

After that, the second diffusion filter unit 162 b performs a non-linearanisotropic diffusion filtering process on the Level 3 output data andthe Level 2 high-frequency decomposed image data by using the diffusiontensor (step S305).

Further, the second adjusting unit 163 b adjusts the signal level of theLevel 2 high-frequency decomposed image data on which the diffusionfiltering process was performed (step S306). The second reconstructingunit 164 b reconstructs (synthesizes the data to obtain) Level 2 outputdata by performing a wavelet inverse transform (step S307). After that,the second reconstructing unit 164 b outputs the Level 2 output data tothe first diffusion filter unit 162 a (step S308), and the process atLevel 2 is thus ended.

After that, the process at Level 1 is performed as shown in FIG. 10.More specifically, as shown in FIG. 10, the first diffusion filter unit162 a judges whether the Level 1 high-frequency decomposed image dataand the Level 2 output data have been obtained (step S401). In thissituation, if the judgment result is in the negative (step S401: No),the first diffusion filter unit 162 a goes into a standby state.

On the contrary, if the Level 1 high-frequency decomposed image data andthe Level 2 output data have been obtained (step S401: Yes), the firstdiffusion filter unit 162 a calculates a structure tensor from the Level2 output data (step S402), and further detects edge information from thestructure tensor (step S403). Subsequently, the first diffusion filterunit 162 a calculates a diffusion tensor from the structure tensor andthe edge information (step S404).

After that, the first diffusion filter unit 162 a performs a non-linearanisotropic diffusion filtering process on the Level 2 output data andthe Level 1 high-frequency decomposed image data by using the diffusiontensor (step S405).

Further, the first adjusting unit 163 a adjusts the signal level of theLevel 1 high-frequency decomposed image data on which the diffusionfiltering process was performed (step S406). The first reconstructingunit 164 a reconstructs (synthesizes the data to obtain) Level 1 outputdata by performing a wavelet inverse transform (step S407). After that,the first reconstructing unit 164 a outputs the Level 1 output data tothe image generating unit 15 as corrected B-mode data (step S408), andthe process at Level 1 is thus ended.

As explained above, according to the present embodiment, similarly tothe conventional method, the edge portion is enhanced by using thenon-linear anisotropic diffusion filters, the speckles are eliminated byperforming the diffusion processes on the portion other than the edgeportion, and further, the processing load required by the non-linearanisotropic filtering processes is reduced by using the multi-resolutionanalysis in combination.

However, according to the present embodiment, unlike the conventionalmethod, the non-linear anisotropic diffusion filters are applied even tothe high-frequency decomposed image data. As a result, according to thepresent embodiment, it is possible to increase the degree of freedom insetting the diffusion filter coefficients. It is therefore possible toadjust the diffusion filter coefficients so as to decrease theoccurrence of the stair-stepped structure in the ultrasound image. As aresult, according to the present embodiment, it is possible to generatean ultrasound image in which the edge is enhanced without causing thefeeling that something is wrong with the ultrasound image and from whichthe speckles are eliminated.

Further, according to the present embodiment, the non-linear anisotropicdiffusion filter is applied to the high-frequency decomposed image data,by using the edge information of either the low-frequency decomposedimage data or the reconstructed data from the level immediatelyunderneath, instead of the edge information of the high-frequencydecomposed image data. In other words, according to the presentembodiment, the diffusion tensor of the high-frequency decomposed imagedata is not calculated, and the diffusion tensor is calculated only onceat each of the hierarchical levels (at each of the levels). Thus, theadditional calculation amount caused by the process of applying thediffusion filter to the high-frequency component is reduced. As aresult, according to the present embodiment, it is possible to performthe calculation at a high speed in the speckle eliminating process.

The exemplary embodiments described above are applicable also to thesituation where the number of levels is an arbitrary natural number thatis two or larger. Also, the exemplary embodiments described above areapplicable to the situation where the multi-resolution analysis isperformed by using a method (e.g., a Laplacian pyramid method) otherthan the “wavelet transform and wavelet inverse transform” method.Further, the exemplary embodiments described above are applicable to thesituation where “B-mode image data” is used as the ultrasound imagedata. Furthermore, the exemplary embodiments described above are alsoapplicable to the situation where the ultrasound image data isthree-dimensional ultrasound image data generated by using a 2D probe ora mechanical scan probe.

The exemplary embodiments above are explained by using the example wherethe processes are performed by the ultrasound diagnosis apparatus.However, the image processing processes explained in the exemplaryembodiments above may be performed by an image processing apparatus thatis provided independently of the ultrasound diagnosis apparatus. Morespecifically, an arrangement is acceptable in which an image processingapparatus having the functions of the image processing unit 16 shown inFIG. 1 receives the ultrasound image data from the ultrasound diagnosisapparatus or from a database of a Picture Archiving and CommunicationSystem (PACS) or a database of an electronic medical record system andperforms the image processing processes described above thereon.Further, such an image processing apparatus may perform the imageprocessing processes explained in the exemplary embodiments above onmedical image data other than ultrasound image data. Examples of suchmedical image data include: X-ray Computed Tomography (CT) image datagenerated by an X-ray CT apparatus; Magnetic Resonance Imaging (MRI)image data generated by an MRI apparatus; and X-ray image data generatedby an X-ray diagnosis apparatus.

The constituent elements of the apparatuses shown in the drawings arebased on functional concepts. Thus, it is not necessary to physicallyconfigure the elements as indicated in the drawings. In other words, thespecific mode of distribution and integration of the apparatuses is notlimited to those shown in the drawings. It is acceptable to functionallyor physically distribute or integrate all or a part of the apparatusesin any arbitrary units, depending on various loads and the status ofuse. Further, all or an arbitrary part of the processing functionsperformed by the apparatuses may be realized by a Central ProcessingUnit (CPU) and a computer program that is analyzed and executed by theCPU or may be realized as hardware using wired logic.

It is possible to realize the image processing method explained in theexemplary embodiments by causing a computer such as a personal computeror a workstation to execute an image processing computer programprepared in advance. It is possible to distribute such an imageprocessing computer program via a network such as the Internet. Further,the image processing computer program may be recorded on acomputer-readable recording medium such as a hard disk, a flexible disk(FD), a Compact Disk Read-Only Memory (CD-ROM), a Magneto-Optical (MO)disk, a Digital Versatile Disk (DVD), or the like, and may be executedas being read by a computer from the recoding medium.

As explained above, according to the present embodiment, it is possibleto generate an ultrasound image in which the edge is enhanced withoutcausing the feeling that something is wrong with the ultrasound imageand from which the speckles are eliminated.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

What is claimed is:
 1. An ultrasound diagnosis apparatus comprising:decomposing circuitry configured to decompose ultrasound image data intolow-frequency decomposed image data and high-frequency decomposed imagedata at each of a predetermined number of hierarchical levels, byperforming a hierarchical multi-resolution analysis; diffusion filtercircuitry configured to apply, at a lowest hierarchical level of thepredetermined number of hierarchical levels, a non-linear anisotropicdiffusion filter to the low-frequency decomposed image data and thehigh-frequency decomposed image data at the lowest hierarchical level,configured to apply, at each of the hierarchical levels higher than thelowest hierarchical level, a non-linear anisotropic diffusion filter todata output from a hierarchical level immediately underneath that hasbeen reconstructed by performing a multi-resolution analysis and to thehigh-frequency decomposed image data at that hierarchical level, andconfigured to generate, for each of the hierarchical levels, edgeinformation of a signal either from the low-frequency decomposed imagedata at the lowest hierarchical level or from the data output from thehierarchical level immediately underneath; adjusting circuitryconfigured to adjust a signal level of the high-frequency decomposedimage data to which the non-linear anisotropic diffusion filter wasapplied, based on the edge information generated by the diffusion filtercircuitry on the same hierarchical level as the hierarchical level ofthe high-frequency decomposed image data at each of the hierarchicallevels; and reconstructing circuitry configured to: reconstruct data byperforming the multi-resolution analysis, from the data that has beenprocessed by the diffusion filter circuitry and was not used in theprocess performed by the adjusting circuitry and the data that has beenprocessed by the adjusting circuitry at the same hierarchical level,output the reconstructed data as the output data to be processed by thediffusion filter circuitry at the hierarchical level immediately above,and obtain the reconstructed data as corrected data of the ultrasoundimage data at the highest hierarchical level, wherein at the lowesthierarchical level, the diffusion filter circuitry applies thenon-linear anisotropic diffusion filter to the high-frequency decomposedimage data at the lowest hierarchical level by using a diffusion filtercoefficient calculated based on a structure tensor and the edgeinformation detected from the low-frequency decomposed image data at thelowest hierarchical level, whereas at each of the hierarchical levelshigher than the lowest hierarchical level, the diffusion filtercircuitry applies the non-linear anisotropic diffusion filter to thehigh-frequency decomposed image data at that hierarchical level by usinga diffusion filter coefficient calculated based on a structure tensorand the edge information detected from the output data that is output bythe reconstructing circuitry at the hierarchical level immediatelyunderneath.
 2. An image processing apparatus comprising: decomposingcircuitry configured to decompose medical image data into low-frequencydecomposed image data and high-frequency decomposed image data at eachof a predetermined number of hierarchical levels, by performing ahierarchical multi-resolution analysis; diffusion filter circuitryconfigured to apply, at a lowest hierarchical level of the predeterminednumber of hierarchical levels, a non-linear anisotropic diffusion filterto the low-frequency decomposed image data and the high-frequencydecomposed image data at the lowest hierarchical level, configured toapply, at each of the hierarchical levels higher than the lowesthierarchical level, a non-linear anisotropic diffusion filter to dataoutput from a hierarchical level immediately underneath that has beenreconstructed by performing a multi-resolution analysis and to thehigh-frequency decomposed image data at that hierarchical level, andconfigured to generate, for each of the hierarchical levels, edgeinformation of a signal either from the low-frequency decomposed imagedata at the lowest hierarchical level or from the data output from thehierarchical level immediately underneath; adjusting circuitryconfigured to adjust a signal level of the high-frequency decomposedimage data to which the non-linear anisotropic diffusion filter wasapplied, based on the edge information generated by the diffusion filtercircuitry on the same hierarchical level as the hierarchical level ofthe high-frequency decomposed image data at each of the hierarchicallevels; and reconstructing circuitry configured to: reconstruct data byperforming the multi-resolution analysis, from the data that has beenprocessed by the diffusion filter circuitry and was not used in theprocess performed by the adjusting circuitry and the data that has beenprocessed by the adjusting circuitry at the same hierarchical level,output the reconstructed data as the output data to be processed by thediffusion filter circuitry at the hierarchical level immediately above,and obtain the reconstructed data as corrected data of the ultrasoundimage data at the highest hierarchical level, wherein at the lowesthierarchical level, the diffusion filter circuitry applies thenon-linear anisotropic diffusion filter to the high-frequency decomposedimage data at the lowest hierarchical level by using a diffusion filtercoefficient calculated based on a structure tensor and the edgeinformation detected from the low-frequency decomposed image data at thelowest hierarchical level, whereas at each of the hierarchical levelshigher than the lowest hierarchical level, the diffusion filtercircuitry applies the non-linear anisotropic diffusion filter to thehigh-frequency decomposed image data at that hierarchical level by usinga diffusion filter coefficient calculated based on a structure tensorand the edge information detected from the output data that is output bythe reconstructing circuitry at the hierarchical level immediatelyunderneath.
 3. An image processing method comprising: a processperformed by decomposing circuitry to decompose medical image data intolow-frequency decomposed image data and high-frequency decomposed imagedata at each of a predetermined number of hierarchical levels, byperforming a hierarchical multi-resolution analysis; a process performedby diffusion filter circuitry to apply, at a lowest hierarchical levelof the predetermined number of hierarchical levels, a non-linearanisotropic diffusion filter to the low-frequency decomposed image dataand the high-frequency decomposed image data at the lowest hierarchicallevel, to apply, at each of the hierarchical levels higher than thelowest hierarchical level, a non-linear anisotropic diffusion filter todata output from a hierarchical level immediately underneath that hasbeen reconstructed by performing a multi-resolution analysis and to thehigh-frequency decomposed image data at that hierarchical level, and togenerate, for each of the hierarchical levels, edge information of asignal either from the low-frequency decomposed image data at the lowesthierarchical level or from the data output from the hierarchical levelimmediately underneath; a process performed by adjusting circuitry toadjust a signal level of the high-frequency decomposed image data towhich the non-linear anisotropic diffusion filter was applied, based onthe edge information generated by the diffusion filter circuitry on thesame hierarchical level as the hierarchical level of the high-frequencydecomposed image data at each of the hierarchical levels; and a processperformed by reconstructing circuitry to: reconstruct data by performingthe multi-resolution analysis, from the data that has been processed bythe diffusion filter circuitry and was not used in the process performedby the adjusting circuitry and the data that has been processed by theadjusting circuitry at the same hierarchical level, output thereconstructed data as the output data to be processed by the diffusionfilter circuitry at the hierarchical level immediately above, and obtainthe reconstructed data as corrected data of the ultrasound image data atthe highest hierarchical level, at the lowest hierarchical level, theprocess performed by the diffusion filter circuitry includes applyingthe non-linear anisotropic diffusion filter to the high-frequencydecomposed image data at the lowest hierarchical level by using adiffusion filter coefficient calculated based on a structure tensor andthe edge information detected from the low-frequency decomposed imagedata at the lowest hierarchical level, whereas at each of thehierarchical levels higher than the lowest hierarchical level, theprocess performed by the diffusion filter circuitry includes applyingthe non-linear anisotropic diffusion filter to the high-frequencydecomposed image data at that hierarchical level by using a diffusionfilter coefficient calculated based on a structure tensor and the edgeinformation detected from the output data that is output by thereconstructing circuitry at the hierarchical level immediatelyunderneath.
 4. The ultrasound diagnosis apparatus according to claim 1,wherein data output from the highest hierarchical level has a resolutionequal to a resolution of the ultrasound image data, data output from alevel underneath the highest hierarchical level has a resolution equalto ¼ the resolution of the ultrasound image data, and data output from alevel underneath the level underneath the highest hierarchical level hasa resolution equal to 1/16 the resolution of the ultrasound image data.